from string import ascii_uppercase

__a    :	List[str]         =    {str(ord(c) - 5_5): c for c in ascii_uppercase}





def 							UpperCAmelCase		(       lowercase				,     lowercase				):







				"""simple docstring"""



				if isinstance(lowercase				,     lowercase				):
								raise TypeError('''int() can\'t convert non-string with explicit base'''				)
				if num < 0:
								raise ValueError('''parameter must be positive int'''				)
				if isinstance(lowercase				,     lowercase				):
								raise TypeError('''\'str\' object cannot be interpreted as an integer'''				)
				if isinstance(lowercase				,     lowercase				):
								raise TypeError('''\'float\' object cannot be interpreted as an integer'''				)
				if base in (0, 1):
								raise ValueError('''base must be >= 2'''				)
				if base > 36:
								raise ValueError('''base must be <= 36'''				)
				__lowercase					     =				''''''
				__lowercase					     =				0
				__lowercase					     =				0
				while div != 1:
								__lowercase     ,	__lowercase					     =				divmod(lowercase				,     lowercase				)
								if base >= 11 and 9 < mod < 36:
												__lowercase					     =				ALPHABET_VALUES[str(lowercase				)]
								else:
												__lowercase					     =				str(lowercase				)
								new_value += actual_value
								__lowercase					     =				num // base
								__lowercase					     =				div
								if div == 0:
												return str(new_value[::-1]				)
								elif div == 1:
												new_value += str(lowercase				)
												return str(new_value[::-1]				)

				return new_value[::-1]


if __name__ == "__main__":
		import doctest

		doctest.testmod()

		for base in range(2, 3_7):
				for num in range(1_0_0_0):
						assert int(decimal_to_any(num, base), base) == num, (
						    num,
						    base,
						    decimal_to_any(num, base),
						    int(decimal_to_any(num, base), base),
						)
import argparse
import json
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    MobileViTConfig,
    MobileViTForImageClassification,
    MobileViTForSemanticSegmentation,
    MobileViTImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
__a    :	Dict         =    logging.get_logger(__name__)





def 							UpperCAmelCase		(       lowercase				):







				"""simple docstring"""



				__lowercase					     =				MobileViTConfig()

				# size of the architecture
				if "mobilevit_s" in mobilevit_name:
								__lowercase					     =				[144, 192, 240]
								__lowercase					     =				[16, 32, 64, 96, 128, 160, 640]
				elif "mobilevit_xs" in mobilevit_name:
								__lowercase					     =				[96, 120, 144]
								__lowercase					     =				[16, 32, 48, 64, 80, 96, 384]
				elif "mobilevit_xxs" in mobilevit_name:
								__lowercase					     =				[64, 80, 96]
								__lowercase					     =				[16, 16, 24, 48, 64, 80, 320]
								__lowercase					     =				0.05
								__lowercase					     =				2.0

				if mobilevit_name.startswith('''deeplabv3_'''				):
								__lowercase					     =				512
								__lowercase					     =				16
								__lowercase					     =				21
								__lowercase					     =				'''pascal-voc-id2label.json'''
				else:
								__lowercase					     =				1000
								__lowercase					     =				'''imagenet-1k-id2label.json'''

				__lowercase					     =				'''huggingface/label-files'''
				__lowercase					     =				json.load(open(hf_hub_download(lowercase				,     lowercase				,     repo_type='''dataset'''				)				,     '''r'''				)				)
				__lowercase					     =				{int(lowercase				): v for k, v in idalabel.items()}
				__lowercase					     =				idalabel
				__lowercase					     =				{v: k for k, v in idalabel.items()}

				return config





def 							UpperCAmelCase		(       lowercase				,     lowercase=False				):







				"""simple docstring"""



				for i in range(1				,     6				):
								if F"layer_{i}." in name:
												__lowercase					     =				name.replace(F"layer_{i}."				,     F"encoder.layer.{i - 1}."				)

				if "conv_1." in name:
								__lowercase					     =				name.replace('''conv_1.'''				,     '''conv_stem.'''				)
				if ".block." in name:
								__lowercase					     =				name.replace('''.block.'''				,     '''.'''				)

				if "exp_1x1" in name:
								__lowercase					     =				name.replace('''exp_1x1'''				,     '''expand_1x1'''				)
				if "red_1x1" in name:
								__lowercase					     =				name.replace('''red_1x1'''				,     '''reduce_1x1'''				)
				if ".local_rep.conv_3x3." in name:
								__lowercase					     =				name.replace('''.local_rep.conv_3x3.'''				,     '''.conv_kxk.'''				)
				if ".local_rep.conv_1x1." in name:
								__lowercase					     =				name.replace('''.local_rep.conv_1x1.'''				,     '''.conv_1x1.'''				)
				if ".norm." in name:
								__lowercase					     =				name.replace('''.norm.'''				,     '''.normalization.'''				)
				if ".conv." in name:
								__lowercase					     =				name.replace('''.conv.'''				,     '''.convolution.'''				)
				if ".conv_proj." in name:
								__lowercase					     =				name.replace('''.conv_proj.'''				,     '''.conv_projection.'''				)

				for i in range(0				,     2				):
								for j in range(0				,     4				):
												if F".{i}.{j}." in name:
																__lowercase					     =				name.replace(F".{i}.{j}."				,     F".{i}.layer.{j}."				)

				for i in range(2				,     6				):
								for j in range(0				,     4				):
												if F".{i}.{j}." in name:
																__lowercase					     =				name.replace(F".{i}.{j}."				,     F".{i}."				)
																if "expand_1x1" in name:
																				__lowercase					     =				name.replace('''expand_1x1'''				,     '''downsampling_layer.expand_1x1'''				)
																if "conv_3x3" in name:
																				__lowercase					     =				name.replace('''conv_3x3'''				,     '''downsampling_layer.conv_3x3'''				)
																if "reduce_1x1" in name:
																				__lowercase					     =				name.replace('''reduce_1x1'''				,     '''downsampling_layer.reduce_1x1'''				)

				for i in range(2				,     5				):
								if F".global_rep.{i}.weight" in name:
												__lowercase					     =				name.replace(F".global_rep.{i}.weight"				,     '''.layernorm.weight'''				)
								if F".global_rep.{i}.bias" in name:
												__lowercase					     =				name.replace(F".global_rep.{i}.bias"				,     '''.layernorm.bias'''				)

				if ".global_rep." in name:
								__lowercase					     =				name.replace('''.global_rep.'''				,     '''.transformer.'''				)
				if ".pre_norm_mha.0." in name:
								__lowercase					     =				name.replace('''.pre_norm_mha.0.'''				,     '''.layernorm_before.'''				)
				if ".pre_norm_mha.1.out_proj." in name:
								__lowercase					     =				name.replace('''.pre_norm_mha.1.out_proj.'''				,     '''.attention.output.dense.'''				)
				if ".pre_norm_ffn.0." in name:
								__lowercase					     =				name.replace('''.pre_norm_ffn.0.'''				,     '''.layernorm_after.'''				)
				if ".pre_norm_ffn.1." in name:
								__lowercase					     =				name.replace('''.pre_norm_ffn.1.'''				,     '''.intermediate.dense.'''				)
				if ".pre_norm_ffn.4." in name:
								__lowercase					     =				name.replace('''.pre_norm_ffn.4.'''				,     '''.output.dense.'''				)
				if ".transformer." in name:
								__lowercase					     =				name.replace('''.transformer.'''				,     '''.transformer.layer.'''				)

				if ".aspp_layer." in name:
								__lowercase					     =				name.replace('''.aspp_layer.'''				,     '''.'''				)
				if ".aspp_pool." in name:
								__lowercase					     =				name.replace('''.aspp_pool.'''				,     '''.'''				)
				if "seg_head." in name:
								__lowercase					     =				name.replace('''seg_head.'''				,     '''segmentation_head.'''				)
				if "segmentation_head.classifier.classifier." in name:
								__lowercase					     =				name.replace('''segmentation_head.classifier.classifier.'''				,     '''segmentation_head.classifier.'''				)

				if "classifier.fc." in name:
								__lowercase					     =				name.replace('''classifier.fc.'''				,     '''classifier.'''				)
				elif (not base_model) and ("segmentation_head." not in name):
								__lowercase					     =				'''mobilevit.''' + name

				return name





def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase=False				):







				"""simple docstring"""



				if base_model:
								__lowercase					     =				''''''
				else:
								__lowercase					     =				'''mobilevit.'''

				for key in orig_state_dict.copy().keys():
								__lowercase					     =				orig_state_dict.pop(lowercase				)

								if key[:8] == "encoder.":
												__lowercase					     =				key[8:]

								if "qkv" in key:
												__lowercase					     =				key.split('''.'''				)
												__lowercase					     =				int(key_split[0][6:]				) - 1
												__lowercase					     =				int(key_split[3]				)
												__lowercase					     =				model.get_submodule(F"{model_prefix}encoder.layer.{layer_num}"				)
												__lowercase					     =				layer.transformer.layer[transformer_num].attention.attention.all_head_size
												__lowercase					     =				(
												    F"{model_prefix}encoder.layer.{layer_num}.transformer.layer.{transformer_num}.attention.attention."
												)
												if "weight" in key:
																__lowercase					     =				val[:dim, :]
																__lowercase					     =				val[dim : dim * 2, :]
																__lowercase					     =				val[-dim:, :]
												else:
																__lowercase					     =				val[:dim]
																__lowercase					     =				val[dim : dim * 2]
																__lowercase					     =				val[-dim:]
								else:
												__lowercase					     =				val

				return orig_state_dict





def 							UpperCAmelCase		(       ):







				"""simple docstring"""



				__lowercase					     =				'''http://images.cocodataset.org/val2017/000000039769.jpg'''
				__lowercase					     =				Image.open(requests.get(lowercase				,     stream=lowercase				).raw				)
				return im





@torch.no_grad()
def 							UpperCAmelCase		(       lowercase				,     lowercase				,     lowercase				,     lowercase=False				):







				"""simple docstring"""



				__lowercase					     =				get_mobilevit_config(lowercase				)

				# load original state_dict
				__lowercase					     =				torch.load(lowercase				,     map_location='''cpu'''				)

				# load 🤗 model
				if mobilevit_name.startswith('''deeplabv3_'''				):
								__lowercase					     =				MobileViTForSemanticSegmentation(lowercase				).eval()
				else:
								__lowercase					     =				MobileViTForImageClassification(lowercase				).eval()

				__lowercase					     =				convert_state_dict(lowercase				,     lowercase				)
				model.load_state_dict(lowercase				)

				# Check outputs on an image, prepared by MobileViTImageProcessor
				__lowercase					     =				MobileViTImageProcessor(crop_size=config.image_size				,     size=config.image_size + 32				)
				__lowercase					     =				image_processor(images=prepare_img()				,     return_tensors='''pt'''				)
				__lowercase					     =				model(**lowercase				)
				__lowercase					     =				outputs.logits

				if mobilevit_name.startswith('''deeplabv3_'''				):
								assert logits.shape == (1, 21, 32, 32)

								if mobilevit_name == "deeplabv3_mobilevit_s":
												__lowercase					     =				torch.tensor(
												    [
												        [[6.2065, 6.1292, 6.2070], [6.1079, 6.1254, 6.1747], [6.0042, 6.1071, 6.1034]],
												        [[-6.9253, -6.8653, -7.0398], [-7.3218, -7.3983, -7.3670], [-7.1961, -7.2482, -7.1569]],
												        [[-4.4723, -4.4348, -4.3769], [-5.3629, -5.4632, -5.4598], [-5.1587, -5.3402, -5.5059]],
												    ]				)
								elif mobilevit_name == "deeplabv3_mobilevit_xs":
												__lowercase					     =				torch.tensor(
												    [
												        [[5.4449, 5.5733, 5.6314], [5.1815, 5.3930, 5.5963], [5.1656, 5.4333, 5.4853]],
												        [[-9.4423, -9.7766, -9.6714], [-9.1581, -9.5720, -9.5519], [-9.1006, -9.6458, -9.5703]],
												        [[-7.7721, -7.3716, -7.1583], [-8.4599, -8.0624, -7.7944], [-8.4172, -7.8366, -7.5025]],
												    ]				)
								elif mobilevit_name == "deeplabv3_mobilevit_xxs":
												__lowercase					     =				torch.tensor(
												    [
												        [[6.9811, 6.9743, 7.3123], [7.1777, 7.1931, 7.3938], [7.5633, 7.8050, 7.8901]],
												        [[-10.5536, -10.2332, -10.2924], [-10.2336, -9.8624, -9.5964], [-10.8840, -10.8158, -10.6659]],
												        [[-3.4938, -3.0631, -2.8620], [-3.4205, -2.8135, -2.6875], [-3.4179, -2.7945, -2.8750]],
												    ]				)
								else:
												raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}"				)

								assert torch.allclose(logits[0, :3, :3, :3]				,     lowercase				,     atol=1E-4				)
				else:
								assert logits.shape == (1, 1000)

								if mobilevit_name == "mobilevit_s":
												__lowercase					     =				torch.tensor([-0.9866, 0.2392, -1.1241]				)
								elif mobilevit_name == "mobilevit_xs":
												__lowercase					     =				torch.tensor([-2.4761, -0.9399, -1.9587]				)
								elif mobilevit_name == "mobilevit_xxs":
												__lowercase					     =				torch.tensor([-1.9364, -1.2327, -0.4653]				)
								else:
												raise ValueError(F"Unknown mobilevit_name: {mobilevit_name}"				)

								assert torch.allclose(logits[0, :3]				,     lowercase				,     atol=1E-4				)

				Path(lowercase				).mkdir(exist_ok=lowercase				)
				print(F"Saving model {mobilevit_name} to {pytorch_dump_folder_path}"				)
				model.save_pretrained(lowercase				)
				print(F"Saving image processor to {pytorch_dump_folder_path}"				)
				image_processor.save_pretrained(lowercase				)

				if push_to_hub:
								__lowercase					     =				{
								    '''mobilevit_s''': '''mobilevit-small''',
								    '''mobilevit_xs''': '''mobilevit-x-small''',
								    '''mobilevit_xxs''': '''mobilevit-xx-small''',
								    '''deeplabv3_mobilevit_s''': '''deeplabv3-mobilevit-small''',
								    '''deeplabv3_mobilevit_xs''': '''deeplabv3-mobilevit-x-small''',
								    '''deeplabv3_mobilevit_xxs''': '''deeplabv3-mobilevit-xx-small''',
								}

								print('''Pushing to the hub...'''				)
								__lowercase					     =				model_mapping[mobilevit_name]
								image_processor.push_to_hub(lowercase				,     organization='''apple'''				)
								model.push_to_hub(lowercase				,     organization='''apple'''				)


if __name__ == "__main__":
		__a    :	int         =    argparse.ArgumentParser()
		# Required parameters
		parser.add_argument(
		    """--mobilevit_name""",
		    default="""mobilevit_s""",
		    type=str,
		    help=(
		        """Name of the MobileViT model you'd like to convert. Should be one of 'mobilevit_s', 'mobilevit_xs',"""
		        """ 'mobilevit_xxs', 'deeplabv3_mobilevit_s', 'deeplabv3_mobilevit_xs', 'deeplabv3_mobilevit_xxs'."""
		    ),
		)
		parser.add_argument(
		    """--checkpoint_path""", required=True, type=str, help="""Path to the original state dict (.pt file)."""
		)
		parser.add_argument(
		    """--pytorch_dump_folder_path""", required=True, type=str, help="""Path to the output PyTorch model directory."""
		)
		parser.add_argument(
		    """--push_to_hub""", action="""store_true""", help="""Whether or not to push the converted model to the 🤗 hub."""
		)

		__a    :	Dict         =    parser.parse_args()
		convert_movilevit_checkpoint(
		    args.mobilevit_name, args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub
		)